Code.ipynb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mini Project - Country Classifiation of Covid-19 tweets\n",
    "\n",
    "Thomas Durkin and Miguel Novo Villar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using state New York server backend.\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\Thomas\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\Thomas\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\Thomas\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\Thomas\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n"
     ]
    },
    {
     "data": {
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       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random as rand\n",
    "import pandas as pd\n",
    "import re\n",
    "import emoji\n",
    "import html\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import swifter\n",
    "import seaborn as sns\n",
    "import translators as ts\n",
    "import warnings\n",
    "import nltk\n",
    "\n",
    "from tqdm import tqdm\n",
    "from bs4 import BeautifulSoup\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from google_trans_new import google_translator\n",
    "from langdetect import detect\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from wordcloud import WordCloud\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.decomposition import NMF, LatentDirichletAllocation\n",
    "from time import time\n",
    "\n",
    "nltk.download('wordnet')\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "nltk.download(\"averaged_perceptron_tagger\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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     "output_type": "stream",
     "text": [
      "C:\\Users\\Thomas\\AppData\\Roaming\\Python\\Python39\\site-packages\\IPython\\core\\interactiveshell.py:3378: DtypeWarning: Columns (3,4) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
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       "        Unnamed: 0                                               text  \\\n",
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "training_df = pd.read_csv(r\"C:/Users/Thomas/Desktop/training_data_new.csv\")\n",
    "training_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "0               0  Ethical investing is not optional anymore, say...   \n",
       "1               1  #COVID19 | Suite à la conférence de presse du ...   \n",
       "2               2  Yesterday, I had a live discussion with @Steve...   \n",
       "3               3  Nepal - #Coronavirus cases up 24% in a week. D...   \n",
       "4               4  American economy jumped up a %  big news story...   \n",
       "...           ...                                                ...   \n",
       "59995       59995  Who knew all we had to do was eat a tenner’s w...   \n",
       "59996       59996  Our death toll of more than 160,000 souls tell...   \n",
       "59997       59997  Apparently, Donald Trump doesn't care about Am...   \n",
       "59998       59998  Is #HulaHooping the perfect #SocialDistancing ...   \n",
       "59999       59999  Leaving the #WHO is a monumentally stupid deci...   \n",
       "\n",
       "      reply_to_screen_name  is_quote  is_retweet  \\\n",
       "0                      NaN     False       False   \n",
       "1                      NaN     False        True   \n",
       "2                      NaN     False        True   \n",
       "3                      NaN     False        True   \n",
       "4                      NaN     False       False   \n",
       "...                    ...       ...         ...   \n",
       "59995                  NaN     False        True   \n",
       "59996                  NaN     False        True   \n",
       "59997                  NaN     False        True   \n",
       "59998                  NaN     False       False   \n",
       "59999                  NaN      True        True   \n",
       "\n",
       "                                  hashtags     Id  \\\n",
       "0                                  covid19      0   \n",
       "1                                  COVID19      1   \n",
       "2                                  COVID19      2   \n",
       "3                              Coronavirus      3   \n",
       "4                      LysolAndCloroxSales      4   \n",
       "...                                    ...    ...   \n",
       "59995                              Covid19  59995   \n",
       "59996                          coronavirus  59996   \n",
       "59997                              COVID19  59997   \n",
       "59998  HulaHooping SocialDistancing London  59998   \n",
       "59999                                  WHO  59999   \n",
       "\n",
       "                                           expanded_urls  \\\n",
       "0      https://www.podbean.com/media/share/pb-uba6x-d...   \n",
       "1      https://twitter.com/gouvernementFR/status/1332...   \n",
       "2      https://www.cnn.com/world/live-news/coronaviru...   \n",
       "3      https://flutrackers.com/forum/forum/-2019-ncov...   \n",
       "4                                                    NaN   \n",
       "...                                                  ...   \n",
       "59995  https://twitter.com/ShaneBeattyNews/status/127...   \n",
       "59996  https://twitter.com/TeamPelosi/status/12919060...   \n",
       "59997                                                NaN   \n",
       "59998  https://www.swlondoner.co.uk/life/08072020-hul...   \n",
       "59999                                                NaN   \n",
       "\n",
       "                                        all_english_text  \n",
       "0      Ethical investing is not optional anymore, say...  \n",
       "1      #COVID19 | Following the Prime Minister's pres...  \n",
       "2      Yesterday, I had a live discussion with @Steve...  \n",
       "3      Nepal - #Coronavirus cases up 24% in a week. D...  \n",
       "4      American economy jumped up a %  big news story...  \n",
       "...                                                  ...  \n",
       "59995  Who knew all we had to do was eat a tenner’s w...  \n",
       "59996  Our death toll of more than 160,000 souls tell...  \n",
       "59997  Apparently, Donald Trump doesn't care about Am...  \n",
       "59998  Is #HulaHooping the perfect #SocialDistancing ...  \n",
       "59999  Leaving the #WHO is a monumentally stupid deci...  \n",
       "\n",
       "[60000 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv(r\"C:/Users/Thomas/Desktop/test_data_new.csv\")\n",
    "test_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part I: Descriptive Analysis - A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a table that contains information on minimum, average, median, and maximum for the following: tweet length (#characters and #words) (text column), hashtag length (#characters and #words) (hashtags column) (Add your table to the report at the end.) (10 points for undergraduate students, 5 points for graduate students)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "204.98047916666667"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the AVERAGE length of tweets (Characters)\n",
    "def tweet_len_char_avg(data):\n",
    "    lenghts = []\n",
    "    for i in data:\n",
    "        lenghts.append(len(i))\n",
    "    return sum(lenghts)/len(data)\n",
    "tweet_len_char_avg(training_df.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "425"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the MAX length of tweets (Characters)\n",
    "def tweet_len_char_max(data):\n",
    "    lenghts = []\n",
    "    for i in data:\n",
    "        lenghts.append(len(i))\n",
    "    return max(lenghts)\n",
    "tweet_len_char_max(training_df.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the MIN length of tweets (Characters)\n",
    "def tweet_len_char_min(data):\n",
    "    lenghts = []\n",
    "    for i in data:\n",
    "        lenghts.append(len(i))\n",
    "    return min(lenghts)\n",
    "tweet_len_char_min(training_df.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "221.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the MEDIAN length of tweets (Characters)\n",
    "def tweet_len_char_median(data):\n",
    "    lenghts = []\n",
    "    for i in data:\n",
    "        lenghts.append(len(i))\n",
    "    return np.median(lenghts)\n",
    "tweet_len_char_median(training_df.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.832408333333333"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the AVERAGE number of words\n",
    "def tweet_len_words_avg(data):\n",
    "    word_count = []\n",
    "    for i in data:\n",
    "        word_count.append(len(i.split()))\n",
    "    return sum(word_count)/len(data)\n",
    "tweet_len_words_avg(training_df.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the MIN number of Words in tweets\n",
    "def tweet_len_words_min(data):\n",
    "    word_count = []\n",
    "    for i in data:\n",
    "        word_count.append(len(i.split()))\n",
    "    return min(word_count)\n",
    "tweet_len_words_min(training_df.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the MAX number of Words in tweets\n",
    "def tweet_len_words_max(data):\n",
    "    word_count = []\n",
    "    for i in data:\n",
    "        word_count.append(len(i.split()))\n",
    "    return max(word_count)\n",
    "tweet_len_words_max(training_df.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the MEDIAN number of Words in tweets\n",
    "def tweet_len_words_max(data):\n",
    "    word_count = []\n",
    "    for i in data:\n",
    "        word_count.append(len(i.split()))\n",
    "    return np.median(word_count)\n",
    "tweet_len_words_max(training_df.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### HASHTHAG COL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.503920833333332"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the AVERAGE length of hashtags (Characters)\n",
    "def hashtag_len_char_avg(data):\n",
    "    lenghts = []\n",
    "    for i in data:\n",
    "        lenghts.append(len(i))\n",
    "    return sum(lenghts)/len(data)\n",
    "hashtag_len_char_avg(training_df.hashtags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the MIN length of hashtags (Characters)\n",
    "def hashtag_len_char_min(data):\n",
    "    lenghts = []\n",
    "    for i in data:\n",
    "        lenghts.append(len(i))\n",
    "    return min(lenghts)\n",
    "hashtag_len_char_min(training_df.hashtags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the MAX length of hashtags (Characters)\n",
    "def hashtag_len_char_max(data):\n",
    "    lenghts = []\n",
    "    for i in data:\n",
    "        lenghts.append(len(i))\n",
    "    return max(lenghts)\n",
    "hashtag_len_char_max(training_df.hashtags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the MEDIAN length of hashtags (Characters)\n",
    "def hashtag_len_char_median(data):\n",
    "    lenghts = []\n",
    "    for i in data:\n",
    "        lenghts.append(len(i))\n",
    "    return np.median(lenghts)\n",
    "hashtag_len_char_median(training_df.hashtags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8100708333333333"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the AVERAGE number of hashtags\n",
    "def hashtags_len_words_avg(data):\n",
    "    word_count = []\n",
    "    for i in data:\n",
    "        word_count.append(len(i.split()))\n",
    "    return sum(word_count)/len(data)\n",
    "hashtags_len_words_avg(training_df.hashtags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the MIN number of hashtags\n",
    "def hashtags_len_words_min(data):\n",
    "    word_count = []\n",
    "    for i in data:\n",
    "        word_count.append(len(i.split()))\n",
    "    return min(word_count)\n",
    "hashtags_len_words_min(training_df.hashtags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the MAX number of hashtags\n",
    "def hashtags_len_words_max(data):\n",
    "    word_count = []\n",
    "    for i in data:\n",
    "        word_count.append(len(i.split()))\n",
    "    return max(word_count)\n",
    "hashtags_len_words_max(training_df.hashtags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the MEDIAN number of hashtags\n",
    "def hashtags_len_words_median(data):\n",
    "    word_count = []\n",
    "    for i in data:\n",
    "        word_count.append(len(i.split()))\n",
    "    return np.median(word_count)\n",
    "hashtags_len_words_median(training_df.hashtags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part I: Descriptive Analysis - B\n",
    "\n",
    "Find the top ten most commonly used hashtags (hashtags column) in the training dataset by\n",
    "calculating the frequency. Then, create a stacked bar chart (one stacked bar per country) which\n",
    "shows the distribution of these ten most commonly used hashtags for each country. Do you\n",
    "observe any patterns? Write your findings in the report. (Add the stacked bar chart to the report\n",
    "at the end.) (10 points for undergraduate students, 5 points for graduate students)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Hashtag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>us</td>\n",
       "      <td>WuhanCoronaVirus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>us</td>\n",
       "      <td>KillerCuomo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>us</td>\n",
       "      <td>Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>us</td>\n",
       "      <td>COVID19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>us</td>\n",
       "      <td>Pakistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>us</td>\n",
       "      <td>GreenStimulus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>us</td>\n",
       "      <td>Nature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>us</td>\n",
       "      <td>Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>us</td>\n",
       "      <td>coronavirus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>us</td>\n",
       "      <td>COVID__19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country           Hashtag\n",
       "0      us  WuhanCoronaVirus\n",
       "1      us       KillerCuomo\n",
       "2      us             Trump\n",
       "3      us           COVID19\n",
       "4      us          Pakistan\n",
       "5      us     GreenStimulus\n",
       "6      us            Nature\n",
       "7      us             Green\n",
       "8      us       coronavirus\n",
       "9      us         COVID__19"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hashtags_df = pd.DataFrame(training_df.hashtags.str.split(' ').tolist(),\n",
    "\t\t\t\t\t\tindex = training_df.country).stack()\n",
    "hashtags_df = hashtags_df.reset_index([0, 'country'])\n",
    "hashtags_df.columns = ['Country', 'Hashtag']\n",
    "\n",
    "hashtags_df[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['COVID19', 'coronavirus', 'Covid19', 'covid19', 'COVID', 'Coronavirus',\n",
       "       'Covid_19', 'COVIDー19', 'Covid', 'StaySafe'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topTenHashtags = hashtags_df['Hashtag'].value_counts()[:10].index\n",
    "topTenHashtags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Hashtag</th>\n",
       "      <th>02JUL20</th>\n",
       "      <th>0613FM_0509</th>\n",
       "      <th>06strong</th>\n",
       "      <th>1-Jun</th>\n",
       "      <th>10-Jul</th>\n",
       "      <th>10-Jun</th>\n",
       "      <th>1000families</th>\n",
       "      <th>1000islands</th>\n",
       "      <th>1000lives</th>\n",
       "      <th>1000names</th>\n",
       "      <th>...</th>\n",
       "      <th>𝐛𝐫𝐞𝐚𝐭𝐡𝐞</th>\n",
       "      <th>𝐟𝐚𝐜𝐞𝐦𝐚𝐬𝐤𝐬</th>\n",
       "      <th>𝐟𝐚𝐜𝐞𝐬𝐡𝐢𝐞𝐥𝐝𝐬</th>\n",
       "      <th>𝐟𝐚𝐢𝐧𝐭</th>\n",
       "      <th>𝐨𝐱𝐲𝐠𝐞𝐧</th>\n",
       "      <th>𝑁𝑎𝑡𝑖𝑜𝑛𝑠𝑈𝑛𝑖𝑡𝑒𝑑</th>\n",
       "      <th>𝒟𝒾𝑒𝒷𝒪37</th>\n",
       "      <th>𝔊𝕯𝔏𝔈</th>\n",
       "      <th>𝗠𝗬𝗠</th>\n",
       "      <th>𝗦𝗢𝗟𝗗</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>australia</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>canada</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ireland</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_zealand</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uk</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>us</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 64237 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Hashtag      02JUL20  0613FM_0509  06strong  1-Jun  10-Jul  10-Jun  \\\n",
       "Country                                                              \n",
       "australia          0            0         0      0       0       0   \n",
       "canada             1            0         0      0       1       1   \n",
       "ireland            0            0         0      0       0       0   \n",
       "new_zealand        0            0         0      0       0       0   \n",
       "uk                 0            0         0      1       0       0   \n",
       "us                 0            1         1      0       0       0   \n",
       "\n",
       "Hashtag      1000families  1000islands  1000lives  1000names  ...  𝐛𝐫𝐞𝐚𝐭𝐡𝐞  \\\n",
       "Country                                                       ...            \n",
       "australia               0            0          0          0  ...        0   \n",
       "canada                  0            1          0          0  ...        0   \n",
       "ireland                 0            0          0          0  ...        1   \n",
       "new_zealand             0            0          0          0  ...        0   \n",
       "uk                      1            0          1          1  ...        0   \n",
       "us                      0            0          0          0  ...        0   \n",
       "\n",
       "Hashtag      𝐟𝐚𝐜𝐞𝐦𝐚𝐬𝐤𝐬  𝐟𝐚𝐜𝐞𝐬𝐡𝐢𝐞𝐥𝐝𝐬  𝐟𝐚𝐢𝐧𝐭  𝐨𝐱𝐲𝐠𝐞𝐧  𝑁𝑎𝑡𝑖𝑜𝑛𝑠𝑈𝑛𝑖𝑡𝑒𝑑  𝒟𝒾𝑒𝒷𝒪37  \\\n",
       "Country                                                                      \n",
       "australia            0            0      0       0              1        0   \n",
       "canada               0            0      0       0              0        0   \n",
       "ireland              2            1      1       1              1        0   \n",
       "new_zealand          0            0      0       0              0        0   \n",
       "uk                   0            0      0       0              0        1   \n",
       "us                   0            0      0       0              1        0   \n",
       "\n",
       "Hashtag      𝔊𝕯𝔏𝔈  𝗠𝗬𝗠  𝗦𝗢𝗟𝗗  \n",
       "Country                       \n",
       "australia       0    0     0  \n",
       "canada          0    1     0  \n",
       "ireland         0    0     0  \n",
       "new_zealand     0    0     1  \n",
       "uk              2    0     0  \n",
       "us              0    0     0  \n",
       "\n",
       "[6 rows x 64237 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countryValues = pd.crosstab(index = hashtags_df['Country'], columns = hashtags_df['Hashtag'])\n",
    "countryValues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Hashtag</th>\n",
       "      <th>COVID19</th>\n",
       "      <th>coronavirus</th>\n",
       "      <th>Covid19</th>\n",
       "      <th>covid19</th>\n",
       "      <th>COVID</th>\n",
       "      <th>Coronavirus</th>\n",
       "      <th>Covid_19</th>\n",
       "      <th>COVIDー19</th>\n",
       "      <th>Covid</th>\n",
       "      <th>StaySafe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>australia</th>\n",
       "      <td>15268</td>\n",
       "      <td>4072</td>\n",
       "      <td>1522</td>\n",
       "      <td>2025</td>\n",
       "      <td>1575</td>\n",
       "      <td>987</td>\n",
       "      <td>1060</td>\n",
       "      <td>819</td>\n",
       "      <td>480</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>canada</th>\n",
       "      <td>15940</td>\n",
       "      <td>4172</td>\n",
       "      <td>1916</td>\n",
       "      <td>1629</td>\n",
       "      <td>1190</td>\n",
       "      <td>1174</td>\n",
       "      <td>1041</td>\n",
       "      <td>895</td>\n",
       "      <td>558</td>\n",
       "      <td>447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ireland</th>\n",
       "      <td>14535</td>\n",
       "      <td>3666</td>\n",
       "      <td>3812</td>\n",
       "      <td>1537</td>\n",
       "      <td>896</td>\n",
       "      <td>861</td>\n",
       "      <td>1386</td>\n",
       "      <td>959</td>\n",
       "      <td>705</td>\n",
       "      <td>1168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_zealand</th>\n",
       "      <td>16680</td>\n",
       "      <td>3571</td>\n",
       "      <td>2303</td>\n",
       "      <td>1493</td>\n",
       "      <td>1764</td>\n",
       "      <td>1044</td>\n",
       "      <td>1158</td>\n",
       "      <td>1078</td>\n",
       "      <td>599</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uk</th>\n",
       "      <td>13734</td>\n",
       "      <td>5292</td>\n",
       "      <td>2036</td>\n",
       "      <td>1454</td>\n",
       "      <td>1279</td>\n",
       "      <td>1375</td>\n",
       "      <td>1127</td>\n",
       "      <td>949</td>\n",
       "      <td>654</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>us</th>\n",
       "      <td>17054</td>\n",
       "      <td>4890</td>\n",
       "      <td>1053</td>\n",
       "      <td>1170</td>\n",
       "      <td>1795</td>\n",
       "      <td>1433</td>\n",
       "      <td>853</td>\n",
       "      <td>890</td>\n",
       "      <td>498</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Hashtag      COVID19  coronavirus  Covid19  covid19  COVID  Coronavirus  \\\n",
       "Country                                                                   \n",
       "australia      15268         4072     1522     2025   1575          987   \n",
       "canada         15940         4172     1916     1629   1190         1174   \n",
       "ireland        14535         3666     3812     1537    896          861   \n",
       "new_zealand    16680         3571     2303     1493   1764         1044   \n",
       "uk             13734         5292     2036     1454   1279         1375   \n",
       "us             17054         4890     1053     1170   1795         1433   \n",
       "\n",
       "Hashtag      Covid_19  COVIDー19  Covid  StaySafe  \n",
       "Country                                           \n",
       "australia        1060       819    480       341  \n",
       "canada           1041       895    558       447  \n",
       "ireland          1386       959    705      1168  \n",
       "new_zealand      1158      1078    599       331  \n",
       "uk               1127       949    654       603  \n",
       "us                853       890    498       210  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countryTopTenValues = countryValues[topTenHashtags]\n",
    "countryTopTenValues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Thomas\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 12540 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\Users\\Thomas\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 12540 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "countryTopTenValues.plot(kind = 'bar', \n",
    "\t\t\t\t\t\t stacked = True,\n",
    "\t\t\t\t\t\t figsize = (15,10))\n",
    "\n",
    "plt.legend(loc=\"lower right\", ncol=2)\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"Top Ten Hashtags by Country\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part I: Descriptive Analysis - C\n",
    "\n",
    "Using the lda_tutorial.pdf file in the assignment folder, perform a Latent Dirichlet Allocation (LDA) analysis to extract the topics in the text column in an unsupervised manner. Set the number of clusters/topics to 10 (ten) (you can adjust other settings to obtain the results that you think work the best). What are your observations? Does each cluster seem to form a meaningful subset? What do they seem to represent? (Add the clusters and your observations to the report at the end.) (10 points for undergraduate students, 5 points for graduate students)\n",
    "\n",
    "### Steps\n",
    "1- Data Cleaning\n",
    "\n",
    "2- EDA\n",
    "\n",
    "3- Prepare data for LDA analysis\n",
    "\n",
    "4- LDA Model Training\n",
    "\n",
    "5- Analyze LDS model results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cleaning the text. \n",
    "# Source: https://www.kaggle.com/code/clmentbisaillon/twitter-customer-support-data-cleaning\n",
    "\n",
    "rare = re.compile(r\"\\^\\S*\")\n",
    "new_line = re.compile(r\"\\n+\\S*\")\n",
    "sig = re.compile(r\"-\\S*\")\n",
    "\n",
    "#Initial preprocessing function\n",
    "def preprocessor(data):\n",
    "    corpus = []\n",
    "    for i in tqdm(range(len(data))):\n",
    "        #remove urls\n",
    "        tweet= re.sub(r'http\\S+', ' ', data[\"all_english_text\"][i])\n",
    "\n",
    "        #remove mentions\n",
    "        tweet = re.sub('@[A-Za-z0–9]+', '', tweet)\n",
    "        tweet = re.sub(\"@[\\w]*\",\"\",tweet)\n",
    "        \n",
    "        # # # Contractions (Source: https://www.kaggle.com/code/gunesevitan/nlp-with-disaster-tweets-eda-cleaning-and-bert/notebook)\n",
    "        tweet = re.sub(r\"he's\", \"he is\", tweet)\n",
    "        tweet = re.sub(r\"there's\", \"there is\", tweet)\n",
    "        tweet = re.sub(r\"We're\", \"We are\", tweet)\n",
    "        tweet = re.sub(r\"That's\", \"That is\", tweet)\n",
    "        tweet = re.sub(r\"won't\", \"will not\", tweet)\n",
    "        tweet = re.sub(r\"they're\", \"they are\", tweet)\n",
    "        tweet = re.sub(r\"Can't\", \"Cannot\", tweet)\n",
    "        tweet = re.sub(r\"wasn't\", \"was not\", tweet)\n",
    "        tweet = re.sub(r\"don\\x89Ûªt\", \"do not\", tweet)\n",
    "        tweet = re.sub(r\"aren't\", \"are not\", tweet)\n",
    "        tweet = re.sub(r\"isn't\", \"is not\", tweet)\n",
    "        tweet = re.sub(r\"What's\", \"What is\", tweet)\n",
    "        tweet = re.sub(r\"haven't\", \"have not\", tweet)\n",
    "        tweet = re.sub(r\"hasn't\", \"has not\", tweet)\n",
    "        tweet = re.sub(r\"There's\", \"There is\", tweet)\n",
    "        tweet = re.sub(r\"He's\", \"He is\", tweet)\n",
    "        tweet = re.sub(r\"It's\", \"It is\", tweet)\n",
    "        tweet = re.sub(r\"You're\", \"You are\", tweet)\n",
    "        tweet = re.sub(r\"I'M\", \"I am\", tweet)\n",
    "        tweet = re.sub(r\"shouldn't\", \"should not\", tweet)\n",
    "        tweet = re.sub(r\"wouldn't\", \"would not\", tweet)\n",
    "        tweet = re.sub(r\"i'm\", \"I am\", tweet)\n",
    "        tweet = re.sub(r\"I\\x89Ûªm\", \"I am\", tweet)\n",
    "        tweet = re.sub(r\"I'm\", \"I am\", tweet)\n",
    "        tweet = re.sub(r\"Isn't\", \"is not\", tweet)\n",
    "        tweet = re.sub(r\"Here's\", \"Here is\", tweet)\n",
    "        tweet = re.sub(r\"you've\", \"you have\", tweet)\n",
    "        tweet = re.sub(r\"you\\x89Ûªve\", \"you have\", tweet)\n",
    "        tweet = re.sub(r\"we're\", \"we are\", tweet)\n",
    "        tweet = re.sub(r\"what's\", \"what is\", tweet)\n",
    "        tweet = re.sub(r\"couldn't\", \"could not\", tweet)\n",
    "        tweet = re.sub(r\"we've\", \"we have\", tweet)\n",
    "        tweet = re.sub(r\"it\\x89Ûªs\", \"it is\", tweet)\n",
    "        tweet = re.sub(r\"doesn\\x89Ûªt\", \"does not\", tweet)\n",
    "        tweet = re.sub(r\"It\\x89Ûªs\", \"It is\", tweet)\n",
    "        tweet = re.sub(r\"Here\\x89Ûªs\", \"Here is\", tweet)\n",
    "        tweet = re.sub(r\"who's\", \"who is\", tweet)\n",
    "        tweet = re.sub(r\"I\\x89Ûªve\", \"I have\", tweet)\n",
    "        tweet = re.sub(r\"y'all\", \"you all\", tweet)\n",
    "        tweet = re.sub(r\"can\\x89Ûªt\", \"cannot\", tweet)\n",
    "        tweet = re.sub(r\"would've\", \"would have\", tweet)\n",
    "        tweet = re.sub(r\"it'll\", \"it will\", tweet)\n",
    "        tweet = re.sub(r\"we'll\", \"we will\", tweet)\n",
    "        tweet = re.sub(r\"wouldn\\x89Ûªt\", \"would not\", tweet)\n",
    "        tweet = re.sub(r\"We've\", \"We have\", tweet)\n",
    "        tweet = re.sub(r\"he'll\", \"he will\", tweet)\n",
    "        tweet = re.sub(r\"Y'all\", \"You all\", tweet)\n",
    "        tweet = re.sub(r\"Weren't\", \"Were not\", tweet)\n",
    "        tweet = re.sub(r\"Didn't\", \"Did not\", tweet)\n",
    "        tweet = re.sub(r\"they'll\", \"they will\", tweet)\n",
    "        tweet = re.sub(r\"they'd\", \"they would\", tweet)\n",
    "        tweet = re.sub(r\"DON'T\", \"DO NOT\", tweet)\n",
    "        tweet = re.sub(r\"That\\x89Ûªs\", \"That is\", tweet)\n",
    "        tweet = re.sub(r\"they've\", \"they have\", tweet)\n",
    "        tweet = re.sub(r\"i'd\", \"I would\", tweet)\n",
    "        tweet = re.sub(r\"should've\", \"should have\", tweet)\n",
    "        tweet = re.sub(r\"You\\x89Ûªre\", \"You are\", tweet)\n",
    "        tweet = re.sub(r\"where's\", \"where is\", tweet)\n",
    "        tweet = re.sub(r\"Don\\x89Ûªt\", \"Do not\", tweet)\n",
    "        tweet = re.sub(r\"we'd\", \"we would\", tweet)\n",
    "        tweet = re.sub(r\"i'll\", \"I will\", tweet)\n",
    "        tweet = re.sub(r\"weren't\", \"were not\", tweet)\n",
    "        tweet = re.sub(r\"They're\", \"They are\", tweet)\n",
    "        tweet = re.sub(r\"Can\\x89Ûªt\", \"Cannot\", tweet)\n",
    "        tweet = re.sub(r\"you\\x89Ûªll\", \"you will\", tweet)\n",
    "        tweet = re.sub(r\"I\\x89Ûªd\", \"I would\", tweet)\n",
    "        tweet = re.sub(r\"let's\", \"let us\", tweet)\n",
    "        tweet = re.sub(r\"it's\", \"it is\", tweet)\n",
    "        tweet = re.sub(r\"can't\", \"cannot\", tweet)\n",
    "        tweet = re.sub(r\"don't\", \"do not\", tweet)\n",
    "        tweet = re.sub(r\"you're\", \"you are\", tweet)\n",
    "        tweet = re.sub(r\"i've\", \"I have\", tweet)\n",
    "        tweet = re.sub(r\"that's\", \"that is\", tweet)\n",
    "        tweet = re.sub(r\"i'll\", \"I will\", tweet)\n",
    "        tweet = re.sub(r\"doesn't\", \"does not\", tweet)\n",
    "        tweet = re.sub(r\"i'd\", \"I would\", tweet)\n",
    "        tweet = re.sub(r\"didn't\", \"did not\", tweet)\n",
    "        tweet = re.sub(r\"ain't\", \"am not\", tweet)\n",
    "        tweet = re.sub(r\"you'll\", \"you will\", tweet)\n",
    "        tweet = re.sub(r\"I've\", \"I have\", tweet)\n",
    "        tweet = re.sub(r\"Don't\", \"do not\", tweet)\n",
    "        tweet = re.sub(r\"I'll\", \"I will\", tweet)\n",
    "        tweet = re.sub(r\"I'd\", \"I would\", tweet)\n",
    "        tweet = re.sub(r\"Let's\", \"Let us\", tweet)\n",
    "        tweet = re.sub(r\"you'd\", \"You would\", tweet)\n",
    "        tweet = re.sub(r\"It's\", \"It is\", tweet)\n",
    "        tweet = re.sub(r\"Ain't\", \"am not\", tweet)\n",
    "        tweet = re.sub(r\"Haven't\", \"Have not\", tweet)\n",
    "        tweet = re.sub(r\"Could've\", \"Could have\", tweet)\n",
    "        tweet = re.sub(r\"youve\", \"you have\", tweet)  \n",
    "        tweet = re.sub(r\"donå«t\", \"do not\", tweet) \n",
    "        \n",
    "        # Character entity references\n",
    "        tweet = re.sub(r\"&gt;\", \"\", tweet)\n",
    "        tweet = re.sub(r\"&lt;\", \"\", tweet)\n",
    "        tweet = re.sub(r\"&amp;\", \"\", tweet)      \n",
    "        \n",
    "        #remove emoji\n",
    "        tweet=re.sub(\"[^\\w\\s#@/:%.,_-]\", \"\", tweet, flags=re.UNICODE)#remove emoji\n",
    "        \n",
    "        tweet = tweet.replace('x', '')\n",
    "        \n",
    "        #remove html tags\n",
    "        tweet = re.sub(r'<.*?>',' ', tweet) \n",
    "        \n",
    "        #rare\n",
    "        tweet = rare.sub(r'', tweet)\n",
    "\n",
    "        #new line\n",
    "        tweet = new_line.sub(r'.', tweet)\n",
    "\n",
    "        #remove digits\n",
    "        tweet = re.sub(r'\\d+',' ', tweet)\n",
    "        \n",
    "        # #remove hashtags\n",
    "        # tweet = re.sub(r'#\\w+',' ', tweet)\n",
    "        \n",
    "        #remove white\n",
    "        tweet = re.sub(\"^\\\\s+|\\\\s+$\", \"\", tweet)  # Remove leading and trailing white space\n",
    "        #unite multispace\n",
    "        tweet = ' '.join(tweet.split())\n",
    "        review = re.sub('[^a-zA-Z]', ' ', tweet)\n",
    "\n",
    "        review = review.lower()\n",
    "        review = review.split()\n",
    "\n",
    "        review = ' '.join(review)\n",
    "        corpus.append(review)\n",
    "    return corpus   \n",
    "\n",
    "#FUNCTIONS APPLIED\n",
    "\n",
    "def convert_list_of_str_to_list_lists(X):\n",
    "    return [list(sentence) for sentence in X]\n",
    "\n",
    "def remove_stopwords(word_tokens):\n",
    "    stop_words = stopwords.words('english')\n",
    "    stop_words.extend([\"amp\",\"wa\",\"ta\",\"ha\",\"nn\",\"ie\",\"ste\"])\n",
    "    return [w for w in word_tokens if not w.lower() in stop_words]\n",
    "\n",
    "def tokenize_words(X, tweet_tokenizer=True, rmv_stopwords=True):\n",
    "    if tweet_tokenizer:\n",
    "        tokenize = TweetTokenizer().tokenize\n",
    "    else:\n",
    "        tokenize = nltk.word_tokenize\n",
    "\n",
    "    word_tokens = [tokenize(sentence) for sentence in tqdm(X)]\n",
    "\n",
    "    if rmv_stopwords:\n",
    "        return [remove_stopwords(tokens) for tokens in tqdm(word_tokens)]\n",
    "    else:\n",
    "        return word_tokens\n",
    "\n",
    "def pos_tagging_words(X):\n",
    "    return [[nltk.pos_tag(sentence)] for sentence in X]\n",
    "\n",
    "def lemmatize_words(X, lemmatizer):\n",
    "    X_lemmatize = list()\n",
    "    for sentence in tqdm(X):\n",
    "        X_lemmatize.append([[lemmatizer.lemmatize(word)] for word in sentence])\n",
    "    return X_lemmatize\n",
    "\n",
    "def prepare_tokens_for_vectorize(X):\n",
    "    X_preproc = list()\n",
    "    for sentence_tok in tqdm(X):\n",
    "        tokens = [tok[0] for tok in tqdm(sentence_tok)]\n",
    "        X_preproc.append(\" \".join(tokens))\n",
    "\n",
    "    return X_preproc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 240000/240000 [00:21<00:00, 11132.73it/s]\n",
      "100%|██████████| 60000/60000 [00:05<00:00, 11192.75it/s]\n",
      "100%|██████████| 240000/240000 [00:19<00:00, 12504.22it/s]\n",
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      "100%|██████████| 60000/60000 [00:10<00:00, 5547.95it/s]\n"
     ]
    }
   ],
   "source": [
    "X_train = preprocessor(training_df)\n",
    "y_train = np.array(training_df[\"country\"])\n",
    "\n",
    "X_test = preprocessor(test_df)\n",
    "\n",
    "X_token_train = tokenize_words(X_train)\n",
    "X_token_test = tokenize_words(X_test)\n",
    "\n",
    "X_pos_tag_train = pos_tagging_words(X_token_train)\n",
    "X_pos_tag_test = pos_tagging_words(X_token_test)\n",
    "\n",
    "X_lemma_train = lemmatize_words(X_token_train, WordNetLemmatizer())\n",
    "X_lemma_test = lemmatize_words(X_token_test, WordNetLemmatizer())\n",
    "\n",
    "X_prep_train = prepare_tokens_for_vectorize(X_lemma_train)\n",
    "X_prep_test = prepare_tokens_for_vectorize(X_lemma_test)\n",
    "\n",
    "#Attach\n",
    "training_df[\"clean_text\"] = np.array(X_prep_train)\n",
    "test_df[\"clean_text\"] = np.array(X_prep_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Join the different processed titles together.\n",
    "long_string = ','.join(list(training_df[\"clean_text\"].values))\n",
    "\n",
    "# # Create a WordCloud object\n",
    "wordcloud = WordCloud(background_color=\"white\", max_words=5000, contour_width=3, contour_color='steelblue')\n",
    "\n",
    "# # Generate a word cloud\n",
    "wordcloud.generate(long_string)\n",
    "\n",
    "# # Visualize the word cloud\n",
    "wordcloud.to_image()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prepate text for LDA\n",
    "\n",
    "# Load the library with the CountVectorizer method\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "\n",
    "# Helper function\n",
    "def plot_10_most_common_words(count_data, count_vectorizer):\n",
    "    # import matplotlib.pyplot as plt\n",
    "    words = count_vectorizer.get_feature_names()\n",
    "    total_counts = np.zeros(len(words))\n",
    "    \n",
    "    for t in count_data:\n",
    "        total_counts+=t.toarray()[0]\n",
    "\n",
    "    count_dict = (zip(words, total_counts))\n",
    "    count_dict = sorted(count_dict, key=lambda x:x[1], reverse=True)[0:10]\n",
    "    words = [w[0] for w in count_dict]\n",
    "    counts = [w[1] for w in count_dict]\n",
    "    x_pos = np.arange(len(words))\n",
    "\n",
    "    plt.figure(2, figsize=(15, 15/1.6180))\n",
    "    plt.subplot(title='10 most common words')\n",
    "    sns.set_context(\"notebook\", font_scale=1.25, rc={\"lines.linewidth\": 2.5})\n",
    "    sns.barplot(x_pos, counts, palette='husl')\n",
    "    plt.xticks(x_pos, words, rotation=90)\n",
    "    plt.xlabel('words')\n",
    "    plt.ylabel('counts')\n",
    "    plt.show()\n",
    "\n",
    "# Initialise the count vectorizer with the English stop words\n",
    "count_vectorizer = CountVectorizer(stop_words='english')\n",
    "# Fit and transform the processed titles\n",
    "count_data = count_vectorizer.fit_transform(training_df[\"clean_text\"].dropna())\n",
    "# Visualise the 10 most common words\n",
    "plot_10_most_common_words(count_data, count_vectorizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import warnings\n",
    "warnings.simplefilter(\"ignore\", DeprecationWarning)\n",
    "\n",
    "# Load the LDA model from sk-learn\n",
    "from sklearn.decomposition import LatentDirichletAllocation as LDA\n",
    "\n",
    "# Helper function\n",
    "def print_topics(model, count_vectorizer, n_top_words):\n",
    "    words = count_vectorizer.get_feature_names()\n",
    "    for topic_idx, topic in tqdm(enumerate(model.components_)):\n",
    "        print(\"\\nTopic #%d:\" % topic_idx)\n",
    "        print(\" \".join([words[i]\n",
    "            for i in topic.argsort()[:-n_top_words - 1:-1]]))\n",
    "\n",
    "# Tweak the two parameters below\n",
    "number_topics = 10\n",
    "number_words = 10\n",
    "\n",
    "# Create and fit the LDA model\n",
    "lda = LDA(n_components=number_topics, n_jobs=-1)\n",
    "lda.fit(count_data)\n",
    "\n",
    "# Print the topics found by the LDA model\n",
    "print(\"Topics found via LDA:\")\n",
    "print_topics(lda, count_vectorizer, number_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%time\n",
    "#from pyLDAvis import sklearn as sklearn_lda\n",
    "#import pickle\n",
    "#import pyLDAvis\n",
    "\n",
    "#LDAvis_data_filepath = os.path.join('./ldavis_prepared_'+str(number_topics))\n",
    "# # this is a bit time consuming - make the if statement True\n",
    "# # if you want to execute visualization prep yourself\n",
    "\n",
    "#if 1 == 1:\n",
    "#    LDAvis_prepared = sklearn_lda.prepare(lda, count_data, count_vectorizer)\n",
    "#    with open(LDAvis_data_filepath, 'wb') as f:\n",
    "#        pickle.dump(LDAvis_prepared, f)\n",
    "\n",
    "# load the pre-prepared pyLDAvis data from disk\n",
    "\n",
    "#with open(LDAvis_data_filepath, 'rb') as f:\n",
    "#    LDAvis_prepared = pickle.load(f)\n",
    "#pyLDAvis.save_html(LDAvis_prepared, './ldavis_prepared_'+ str(number_topics) +'.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer: Topics\n",
    "Topics found via LDA:\n",
    "\n",
    "Topic #0: covid report new zealand day lockdown coronavirus today patient data\n",
    "\n",
    "Topic #1: covid pandemic vaccine support new help coronavirus health business impact\n",
    "\n",
    "Topic #2: covid case death new day coronavirus number people test positive\n",
    "\n",
    "Topic #3: coronavirus covid uk test flu government positive people quarantine trump\n",
    "\n",
    "Topic #4: covid dr coronavirus treatment fauci hydroychloroquine study virus immunity say\n",
    "\n",
    "Topic #5: covid home stayhome worker school staysafe care family life people\n",
    "\n",
    "Topic #6: covid mask people coronavirus spread face wear hand social distancing\n",
    "\n",
    "Topic #7: covid coronavirus minister china lockdown say week day medical uk\n",
    "\n",
    "Topic #8: covid trump coronavirus american people died america staysafe vote know\n",
    "\n",
    "Topic #9: covid pandemic need health people crisis government response economy world"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part I: Descriptive Analysis - D\n",
    "Using the code in the following link4, perform Non-negative Matrix Factorization for topic analysis. Again, like in question c), set the number of clusters/topics to 10 (ten) and extract the topics in an unsupervised manner. Adjust any parameters as you see fit. Analyze the results. Do you see any similarities or differences with respect to your results in c) ? Explain. (5 points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(ngram_range=(1,2), lowercase=False)\n",
    "vectorizer.fit(X_prep_train+X_prep_test)\n",
    "\n",
    "X_final_train = vectorizer.transform(X_prep_train)\n",
    "X_final_test = vectorizer.transform(X_prep_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 2000\n",
    "n_features = 1000\n",
    "n_components = 10\n",
    "n_top_words = 20\n",
    "batch_size = 128\n",
    "init = \"nndsvda\"\n",
    "\n",
    "\n",
    "def plot_top_words(model, feature_names, n_top_words, title):\n",
    "    fig, axes = plt.subplots(2, 5, figsize=(30, 15), sharex=True)\n",
    "    axes = axes.flatten()\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        top_features_ind = topic.argsort()[: -n_top_words - 1 : -1]\n",
    "        top_features = [feature_names[i] for i in top_features_ind]\n",
    "        weights = topic[top_features_ind]\n",
    "\n",
    "        ax = axes[topic_idx]\n",
    "        ax.barh(top_features, weights, height=0.7)\n",
    "        ax.set_title(f\"Topic {topic_idx +1}\", fontdict={\"fontsize\": 30})\n",
    "        ax.invert_yaxis()\n",
    "        ax.tick_params(axis=\"both\", which=\"major\", labelsize=20)\n",
    "        for i in \"top right left\".split():\n",
    "            ax.spines[i].set_visible(False)\n",
    "        fig.suptitle(title, fontsize=40)\n",
    "\n",
    "    plt.subplots_adjust(top=0.90, bottom=0.05, wspace=0.90, hspace=0.3)\n",
    "    plt.show()\n",
    "\n",
    "# Use tf-idf features for NMF.\n",
    "print(\"Extracting tf-idf features for NMF...\")\n",
    "tfidf_vectorizer = TfidfVectorizer(\n",
    "    max_df=0.95, min_df=2, max_features=n_features, stop_words=\"english\"\n",
    ")\n",
    "t0 = time()\n",
    "tfidf = tfidf_vectorizer.fit_transform(X_prep_train)\n",
    "print(\"done in %0.3fs.\" % (time() - t0))\n",
    "\n",
    "# Use tf (raw term count) features for LDA.\n",
    "print(\"Extracting tf features for LDA...\")\n",
    "tf_vectorizer = CountVectorizer(\n",
    "    max_df=0.95, min_df=2, max_features=n_features, stop_words=\"english\"\n",
    ")\n",
    "t0 = time()\n",
    "tf = tf_vectorizer.fit_transform(X_prep_train)\n",
    "print(\"done in %0.3fs.\" % (time() - t0))\n",
    "print()\n",
    "\n",
    "# Fit the NMF model\n",
    "print(\n",
    "    \"Fitting the NMF model (Frobenius norm) with tf-idf features, \"\n",
    "    \"n_samples=%d and n_features=%d...\" % (n_samples, n_features)\n",
    ")\n",
    "t0 = time()\n",
    "nmf = NMF(\n",
    "    n_components=n_components,\n",
    "    random_state=1,\n",
    "    init=init,\n",
    "    beta_loss=\"frobenius\",\n",
    "    alpha_W=0.00005,\n",
    "    alpha_H=0.00005,\n",
    "    l1_ratio=1,\n",
    ").fit(tfidf)\n",
    "print(\"done in %0.3fs.\" % (time() - t0))\n",
    "\n",
    "\n",
    "tfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\n",
    "plot_top_words(\n",
    "    nmf, tfidf_feature_names, n_top_words, \"Topics in NMF model (Frobenius norm)\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part I: Descriptive Analysis - E\n",
    "Write a ‘text cleaner’ function that does the following: (i) remove stopwords5, (ii) remove all words that are shorter than 3 characters, (iii) remove all links (starting with http), (iv) remove emojis, (v) remove punctuation. Attach the code you wrote to the lemmatizer.py file in the project folder. Run the lemmatizer function and create ‘cleaned and lemmatized’ version of text column. (You can name the new column as text_clean). After the cleaning, expand the table you have created in Part I, a) by calculating minimum, average, median, and maximum for the newly created text_clean column (#characters and #words). (5 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same text cleaner as used in Part C."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate the AVERAGE lenght of CLEAN tweets (Characters)\n",
    "def tweet_len_char_avg(data):\n",
    "    lenghts = []\n",
    "    for i in data:\n",
    "        lenghts.append(len(i))\n",
    "    return sum(lenghts)/len(data)\n",
    "tweet_len_char_avg(training_df.clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate the MAX lenght of CLEAN tweets (Characters)\n",
    "def tweet_len_char_max(data):\n",
    "    lenghts = []\n",
    "    for i in data:\n",
    "        lenghts.append(len(i))\n",
    "    return max(lenghts)\n",
    "tweet_len_char_max(training_df.clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate the MIN lenght of CLEAN tweets (Characters)\n",
    "def tweet_len_char_min(data):\n",
    "    lenghts = []\n",
    "    for i in data:\n",
    "        lenghts.append(len(i))\n",
    "    return min(lenghts)\n",
    "tweet_len_char_min(training_df.clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate the MEDIAN lenght of CLEAN tweets (Characters)\n",
    "def tweet_len_char_median(data):\n",
    "    lenghts = []\n",
    "    for i in data:\n",
    "        lenghts.append(len(i))\n",
    "    return np.median(lenghts)\n",
    "tweet_len_char_median(training_df.clean_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate the AVERAGE number of CLEAN words\n",
    "def tweet_len_words_avg(data):\n",
    "    word_count = []\n",
    "    for i in data:\n",
    "        word_count.append(len(i.split()))\n",
    "    return sum(word_count)/len(data)\n",
    "tweet_len_words_avg(training_df.clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate the MIN number of CLEAN Words in tweets\n",
    "def tweet_len_words_min(data):\n",
    "    word_count = []\n",
    "    for i in data:\n",
    "        word_count.append(len(i.split()))\n",
    "    return min(word_count)\n",
    "tweet_len_words_min(training_df.clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate the MAX number of CLEAN Words in tweets\n",
    "def tweet_len_words_max(data):\n",
    "    word_count = []\n",
    "    for i in data:\n",
    "        word_count.append(len(i.split()))\n",
    "    return max(word_count)\n",
    "tweet_len_words_max(training_df.clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate the MEDIAN number of CLEAN Words in tweets\n",
    "def tweet_len_words_max(data):\n",
    "    word_count = []\n",
    "    for i in data:\n",
    "        word_count.append(len(i.split()))\n",
    "    return np.median(word_count)\n",
    "tweet_len_words_max(training_df.clean_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part I: Descriptive Analysis - F\n",
    "\n",
    "Extract the unique set of all different hashtags found in the training dataset (i). Then, create one\n",
    "separate data subset for each country by splitting the data using the country column (ii). As\n",
    "next, for each country, create a vectorized format of the count values for all different hashtags\n",
    "you have extracted in (i) (you should obtain one count vector per country with all unique\n",
    "hashtags). Using cosine similarity6\n",
    ", compute the pairwise similarity values between the different\n",
    "count vectors you have created for each country. Finally, create a heatmap7 of the pairwise\n",
    "similarity values you have calculated. What are some of your observations? Are any two countries\n",
    "more similar to each other than others? If yes, explain. If not, explain, as well. Are there any very\n",
    "large or very small values? (Add the graph you created to the report at the end.) (5 points)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=['australia', 'canada', 'ireland', 'new zealand', 'uk', 'us']\n",
    "similarity = cosine_similarity(countryValues)\n",
    "\n",
    "mask = np.zeros_like(similarity)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "sns.heatmap(similarity, square=True, annot=True, xticklabels=labels, yticklabels=labels, mask=mask)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "session = requests.Session()\n",
    "#expanding URLs\n",
    "def expandUrls(tweet):\n",
    "\turls = re.findall(r'(https?://\\S+)', tweet)\n",
    "\texpanded = []\n",
    "\tfor url in urls:\n",
    "\t\ttry:\n",
    "\t\t\texpanded.append(session.head(url, allow_redirects=True, timeout=3).url)\n",
    "\t\texcept:\n",
    "\t\t\texpanded.append(url)\n",
    "\treturn expanded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib.parse import urlparse\n",
    "\n",
    "#Function to extract words from expanded link\n",
    "def getUrlWords(urls):\n",
    "    info = []\n",
    "    urls = urls.split(\" \")\n",
    "\t# Split up words in the path\n",
    "    if(urls != ['']):\n",
    "        for url in urls:\n",
    "            parsed_url = urlparse(url)\n",
    "            split_slash = \" \".join(parsed_url.path.split(\"/\"))\n",
    "            info.append(\" \".join(split_slash.split('-')))\n",
    "            \n",
    "\t\t\t#Split up words in website location\n",
    "            if (parsed_url.netloc not in ['twitter.com', 't.co']):\n",
    "                info.append(\" \".join(parsed_url.netloc.split(\".\")))\n",
    "\n",
    "    return \" \".join(info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['https://paper.li/JacobTh85649736/1584426982?edition_id=5fe8a2a0-c40a-11ea-a3f9-0cc47a0d1605']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expandUrls(training_df['text'][235000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CODE TO RUN EXPAND LINKS FUNCTION\n",
    "\n",
    "#expanded_urls = []                   \n",
    "#for i in tqdm(range(len(training_df))):\n",
    "\t#expanded_urls.append(expandUrls(training_df['text'][i]))\n",
    "#training_df['expanded_urls'] = expanded_urls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pandas Apply: 100%|██████████| 240000/240000 [00:00<00:00, 1042579.00it/s]\n",
      "Pandas Apply: 100%|██████████| 240000/240000 [00:08<00:00, 27278.71it/s]\n"
     ]
    }
   ],
   "source": [
    "training_df['text_remove_newline'] = training_df['text'].swifter.apply(lambda x: x.replace(\"\\n\", \"\"))\n",
    "training_df['text_demojize'] = training_df['text_remove_newline'].swifter.apply(lambda x: emoji.demojize(x, delimiters=(\" \", \" \")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pandas Apply: 100%|██████████| 240000/240000 [00:01<00:00, 185886.42it/s]\n",
      "Pandas Apply: 100%|██████████| 240000/240000 [00:02<00:00, 116602.75it/s]\n"
     ]
    }
   ],
   "source": [
    "training_df['expanded_urls'] = training_df['expanded_urls'].fillna(\"\")\n",
    "training_df['expanded_urls'] = training_df['expanded_urls'].apply(lambda x: x.strip(\" \"))\n",
    "training_df['url_info'] = training_df['expanded_urls'].swifter.apply(lambda x: getUrlWords(x))\n",
    "\n",
    "training_df['new_text'] = training_df['text_demojize'] + training_df['url_info']\n",
    "training_df['final_text'] = training_df['new_text'].swifter.apply(lambda x: re.sub(r'\\s+', ' ', x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'#Pakistan (#GreenStimulus = #Nature protection + #Green Jobs for community youth) - within this the recently announced #ProtectedAreasInitiative by @ImranKhanPTI is a major part of the post #COVID19 recovery plan https://t.co/pqsoCQ83DZ item 20200717070925 r4m9m news trust org'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_df['final_text'][3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multinomial Bayes Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = training_df['final_text']\n",
    "y = training_df['country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size=0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_pipeline = Pipeline([\n",
    "  (\"tf-idf\", TfidfVectorizer(sublinear_tf = True, \n",
    "  \t\t\t\t\t\t\tstop_words = 'english')),\n",
    "  (\"nb\", MultinomialNB())\n",
    "])\n",
    "nb_pipeline.fit(x_train, y_train)\n",
    "\n",
    "y_pred_nb = nb_pipeline.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = nb_pipeline.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels=['australia', 'canada', 'ireland', 'new zealand', 'uk', 'us']\n",
    "\n",
    "display = plot_confusion_matrix(\n",
    "        nb_pipeline,\n",
    "        x_test,\n",
    "        y_test,\n",
    "        display_labels=labels,\n",
    "        cmap=plt.cm.Blues,\n",
    "\t\txticks_rotation=45)\n",
    "plt.show()\n",
    "                    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1D CNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras_preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Conv1D, GlobalMaxPooling1D, Embedding\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "numWords = 100000\n",
    "max_length = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer(num_words=numWords, filters='!\"#$%&()*+,-./:;<=>?@[\\]^_`{|}~', lower=True)\n",
    "tokenizer.fit_on_texts(training_df['final_text'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = tokenizer.texts_to_sequences(training_df['final_text'].values)\n",
    "X = pad_sequences(X, maxlen=max_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "training_df['Country_Cat'] = label_encoder.fit_transform(training_df['country'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X[x_train.index]\n",
    "X_test = X[x_test.index]\n",
    "Y_train = to_categorical(training_df['Country_Cat'][y_train.index])\n",
    "Y_test = to_categorical(training_df['Country_Cat'][y_test.index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Embedding output dimensions = 100\n",
    "#1D CNN layer with 250 filters and a kernel size of 3\n",
    "model = Sequential()\n",
    "model.add(Embedding(numWords, 100, input_length=max_length))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Conv1D(250,\n",
    "                 3,\n",
    "                 padding='valid',\n",
    "                 activation='relu',\n",
    "                 strides=1))\n",
    "model.add(GlobalMaxPooling1D())\n",
    "model.add(Dense(250))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(6, activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "#print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "1200/1200 [==============================] - 123s 102ms/step - loss: 1.4481 - accuracy: 0.4160 - val_loss: 1.3322 - val_accuracy: 0.4813\n",
      "Epoch 2/2\n",
      "1200/1200 [==============================] - 122s 102ms/step - loss: 1.1726 - accuracy: 0.5563 - val_loss: 1.3553 - val_accuracy: 0.4796\n"
     ]
    }
   ],
   "source": [
    "epochs = 2\n",
    "batch_size = 128\n",
    "\n",
    "history = model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size,validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500/1500 [==============================] - 5s 4ms/step - loss: 1.3577 - accuracy: 0.4824\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.357701063156128, 0.48243749141693115]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500/1500 [==============================] - 5s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred_cnn = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "y_pred = np.argmax(y_pred_cnn, axis=1)\n",
    "\n",
    "labels=['australia', 'canada', 'ireland', 'new zealand', 'uk', 'us']\n",
    "cm = confusion_matrix(training_df['Country_Cat'][y_test.index].to_numpy(), y_pred, labels = [0,1,2,3,4,5])\n",
    "\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
    "disp.plot(xticks_rotation = 45, cmap=plt.cm.Blues)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensemble Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = y_pred_cnn + y_pred_nb\n",
    "res = np.argmax(y_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4949375"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(res, training_df['Country_Cat'][y_test.index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels=['australia', 'canada', 'ireland', 'new zealand', 'uk', 'us']\n",
    "cm = confusion_matrix(training_df['Country_Cat'][y_test.index].to_numpy(), res, labels = [0,1,2,3,4,5])\n",
    "\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
    "disp.plot(xticks_rotation = 45, cmap=plt.cm.Blues)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pandas Apply: 100%|██████████| 60000/60000 [00:00<00:00, 1007543.78it/s]\n",
      "Pandas Apply: 100%|██████████| 60000/60000 [00:02<00:00, 27530.82it/s]\n",
      "Pandas Apply: 100%|██████████| 60000/60000 [00:00<00:00, 119777.03it/s]\n"
     ]
    }
   ],
   "source": [
    "test_df['text_remove_newline'] = test_df['text'].swifter.apply(lambda x: x.replace(\"\\n\", \"\"))\n",
    "test_df['text_demojize'] = test_df['text_remove_newline'].swifter.apply(lambda x: emoji.demojize(x, delimiters=(\" \", \" \")))\n",
    "\t\n",
    "test_df['expanded_urls'] = test_df['expanded_urls'].fillna(\"\")\n",
    "test_df['expanded_urls'] = test_df['expanded_urls'].apply(lambda x: x.strip(\" \"))\n",
    "\n",
    "test_df['url_info'] = test_df['expanded_urls'].apply(lambda x: getUrlWords(x))\n",
    "test_df['new_text'] = test_df['text_demojize'] + test_df['url_info']\n",
    "test_df['final_text'] = test_df['new_text'].swifter.apply(lambda x: re.sub(r'\\s+', ' ', x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Pls RT: An upcoming Report by @Kaleidoscope_T and @CWEquality is exposing the devastating ways in which the #COVID19 pandemic is affecting our #LGBTI+ siblings in the #Commonwealth. Please support and donate if you can https://t.co/tH7iFwLIjN #fundraising #humanrights #campaign https://t.co/lJ35xdhHaj covid 19 www kaleidoscopetrust com MsLadyPhyll status 1258470166314303489 photo 1'"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['final_text'][11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df_tokenized = tokenizer.texts_to_sequences(test_df['final_text'].values)\n",
    "test_df_padded = pad_sequences(test_df_tokenized, maxlen=max_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 7s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "test_y_pred_cnn = model.predict(test_df_padded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_y_pred_nb = nb_pipeline.predict_proba(test_df['final_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_y_pred = test_y_pred_cnn + test_y_pred_nb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_y_pred = np.argmax(final_y_pred, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making A Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>new_zealand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>ireland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>new_zealand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>australia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id     Category\n",
       "0   0       canada\n",
       "1   1       canada\n",
       "2   2           us\n",
       "3   3       canada\n",
       "4   4  new_zealand\n",
       "5   5      ireland\n",
       "6   6           us\n",
       "7   7           us\n",
       "8   8  new_zealand\n",
       "9   9    australia"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.DataFrame()\n",
    "submission[\"Id\"] = test_df[\"Id\"]\n",
    "submission[\"Category\"] = label_encoder.inverse_transform(final_y_pred)\n",
    "submission.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(r\"C:/Users/Thomas/Desktop/submission.csv\", index=False)"
   ]
  }
 ],
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